Art reinterpretation is the practice of creating a variation of a reference work, making a paired artwork that exhibits a distinct artistic style. We ask if such an image pair can be used to customize a generative model to capture the demonstrated stylistic difference. We propose Pair Customization, a new customization method that learns stylistic difference from a single image pair and then applies the acquired style to the generation process. Unlike existing methods that learn to mimic a single concept from a collection of images, our method captures the stylistic difference between paired images. This allows us to apply a stylistic change without overfitting to the specific image content in the examples. To address this new task, we employ a joint optimization method that explicitly separates the style and content into distinct LoRA weight spaces. We optimize these style and content weights to reproduce the style and content images while encouraging their orthogonality. During inference, we modify the diffusion process via a new style guidance based on our learned weights. Both qualitative and quantitative experiments show that our method can effectively learn style while avoiding overfitting to image content, highlighting the potential of modeling such stylistic differences from a single image pair.

翻译：艺术再诠释是指创作参考作品的变体，生成展现独特艺术风格的配对艺术作品。本文探讨此类图像对能否用于定制生成模型，以捕捉所展示的风格差异。我们提出配对定制方法——一种新颖的定制技术，可从单张图像对学习风格差异，并将习得的风格应用于生成过程。与现有方法从图像集合学习模仿单一概念不同，本方法捕捉配对图像间的风格差异。这使得我们能够施加风格变化，而不会对示例中的特定图像内容产生过拟合。针对这一新任务，我们采用联合优化方法，将风格与内容显式分离至不同的LoRA权重空间。通过优化这些风格与内容权重来重建风格图像和内容图像，同时促进二者的正交性。在推理阶段，我们基于习得的权重通过新型风格引导机制修正扩散过程。定性与定量实验均表明，本方法能有效学习风格并避免对图像内容的过拟合，凸显了从单张图像对建模此类风格差异的潜力。